This paper presents a generalizable data-driven computational electromagnetics (CEM) framework leveraging graph neural networks (GNNs). The proposed model supports training and inference for CEM scenarios with different simulation domain sizes and electromagnetic properties, while exploiting the locality of GNNs to achieve reduced complexity and enhanced accuracy. Our results indicate that GNNs can successfully infer the electromagnetic field spatiotemporal evolution for arbitrary simulation domain setups, paving the way for fully-fledged data-driven CEM models.